Data acquisition and storage capabilities are growing exponentially with no signs of slowing. According to a recent study, more than 100 billion photographs are taken each year. To store them all digitally would require 500 petabytes of storage. With both digital cameras and cell phones that can capture video streams, this same content explosion is occurring with video. From the medical radiology field, the entertainment industry, the government agencies for the national security, to the NASA Goddard space center's earth science project, huge amounts of imagery data and video stream data are being created and stored every day. In recent years, annual doubling in data storage capability has been the rule. In contrast, resources such as experienced minds, money, time and patience to analyze data are relatively fixed.
Currently, one of the major challenges in managing the huge amount of video stream data is related to matching a specific video stream against a library of existing video streams. Using existing methods, the search is performed with user defined inefficient queries, color histograms, color moments or other simple image characterization methods for key frames within the video stream. This traditional search puts significant burden on users to define effective queries. In addition, the traditional method does not provide an efficient means for searching today's vast quantities of video stream data.
Another significant challenge is that of overcoming video pirating or the use of proprietary or classified video imagery data without approval. Many methods have been attempted to automatically classify video content so that unauthorized use can be detected and stopped, but all these methods have been cumbersome and inaccurate.
There is, therefore, a need for solutions that perform effective and efficient matching of video streams or portions of video stream against a library of video streams stored in a video stream database.